Logistic Regression manual.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a1d7457",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e506af98",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(r\"C:\\Users\\ashuv\\Desktop\\csv\\titanic_test.csv\")\n",
    "train = pd.read_csv(r\"C:\\Users\\ashuv\\Desktop\\csv\\titanic_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d29dfcec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch   \n",
       "0              1         0       3    male  22.0      1      0  \\\n",
       "1              2         1       1  female  38.0      1      0   \n",
       "2              3         1       3  female  26.0      0      0   \n",
       "3              4         1       1  female  35.0      1      0   \n",
       "4              5         0       3    male  35.0      0      0   \n",
       "..           ...       ...     ...     ...   ...    ...    ...   \n",
       "886          887         0       2    male  27.0      0      0   \n",
       "887          888         1       1  female  19.0      0      0   \n",
       "888          889         0       3  female   NaN      1      2   \n",
       "889          890         1       1    male  26.0      0      0   \n",
       "890          891         0       3    male  32.0      0      0   \n",
       "\n",
       "               Ticket     Fare Cabin Embarked  \n",
       "0           A/5 21171   7.2500   NaN        S  \n",
       "1            PC 17599  71.2833   C85        C  \n",
       "2    STON/O2. 3101282   7.9250   NaN        S  \n",
       "3              113803  53.1000  C123        S  \n",
       "4              373450   8.0500   NaN        S  \n",
       "..                ...      ...   ...      ...  \n",
       "886            211536  13.0000   NaN        S  \n",
       "887            112053  30.0000   B42        S  \n",
       "888        W./C. 6607  23.4500   NaN        S  \n",
       "889            111369  30.0000  C148        C  \n",
       "890            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train.drop('Name', axis =1)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cedc3482",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/(1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "581700b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = X_train.shape[1]\n",
    "n = X_train.shape[0]\n",
    "\n",
    "W = np.zeros((n,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "38647fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca215426",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X, Y, learning_rate, iterations):\n",
    "    \n",
    "    m = X_train.shape[1]\n",
    "    n = X_train.shape[0]\n",
    "    \n",
    "    W = np.zeros((n,1))\n",
    "    B = 0\n",
    "    \n",
    "    cost_list = []\n",
    "    \n",
    "    for i in range(iterations):\n",
    "        \n",
    "        Z = np.dot(W.T, X) + B\n",
    "        A = sigmoid(Z)\n",
    "        \n",
    "        # cost function\n",
    "        cost = -(1/m)*np.sum( Y*np.log(A) + (1-Y)*np.log(1-A))\n",
    "        \n",
    "        # Gradient Descent\n",
    "        dW = (1/m)*np.dot(A-Y, X.T)\n",
    "        dB = (1/m)*np.sum(A - Y)\n",
    "        \n",
    "        W = W - learning_rate*dW.T\n",
    "        B = B - learning_rate*dB\n",
    "        \n",
    "        # Keeping track of our cost function value\n",
    "        cost_list.append(cost)\n",
    "        \n",
    "        if(i%(iterations/10) == 0):\n",
    "            print(\"cost after \", i, \"iteration is : \", cost)\n",
    "        \n",
    "    return W, B, cost_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0a59fdba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost after  0 iteration is :  0.6931471805599454\n",
      "cost after  10000 iteration is :  0.4965277769389531\n",
      "cost after  20000 iteration is :  0.46674868550666\n",
      "cost after  30000 iteration is :  0.45687787762434423\n",
      "cost after  40000 iteration is :  0.45288994293089646\n",
      "cost after  50000 iteration is :  0.4509326025222643\n",
      "cost after  60000 iteration is :  0.44977087490094686\n",
      "cost after  70000 iteration is :  0.4489640829216278\n",
      "cost after  80000 iteration is :  0.44834126966124827\n",
      "cost after  90000 iteration is :  0.44783045246935776\n"
     ]
    }
   ],
   "source": [
    "iterations = 100000\n",
    "learning_rate = 0.0015\n",
    "W, B, cost_list = model(X_train, Y_train, learning_rate = learning_rate, iterations = iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "03b9d201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(iterations), cost_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a913c218",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(X, Y, W, B):\n",
    "    \n",
    "    Z = np.dot(W.T, X) + B\n",
    "    A = sigmoid(Z)\n",
    "    \n",
    "    A = A > 0.5\n",
    "    \n",
    "    A = np.array(A, dtype = 'int64')\n",
    "    \n",
    "    acc = (1 - np.sum(np.absolute(A - Y))/Y.shape[1])*100\n",
    "    \n",
    "    print(\"Accuracy of the model is : \", round(acc, 2), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d04b8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model is :  91.39 %\n"
     ]
    }
   ],
   "source": [
    "accuracy(X_test, Y_test, W, B)"
   ]
  }
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